On this episode, Jean-Sébastien Charest, chief information officer at the Business Development Bank of Canada discusses Canada’s productivity crisis. Canada’s labour productivity, which is measured by the real gross domestic product (GDP) by hours worked across the economy, has been compared with the United States and other advanced economies, and Canada is lagging behind.

Charest believes one key factor to Canada’s falling productivity is the underinvestment in new technologies, particularly the hesitance of Canadian businesses to embrace artificial intelligence (AI), especially among small and medium enterprises where AI adoption is slowest.

Canada is ranked 20th among 35 OECD (Organization for Economic Co-operation and Development) countries in AI adoption, so Charest outlines a practical “three horizon” strategy for AI integration to help these Canadian SMEs catch up.

This episode is a useful resource for any entrepreneur, business leader or professional looking to boost revenue and cut costs. Listen now to learn how AI can help close Canada’s productivity gap and its role as a transformative tool in reshaping the country’s businesses and economy.

Neil Morrison:

Welcome to Foresight, the CPA podcast. I'm Neil Morrison.

It's shocking. It's a crisis. It's scary. These are just some of the ways experts have described the productivity challenge Canada is facing. And any way you look at it, Canada is falling behind comparable countries on this crucial measure, and the decline is steep. One of the reasons Canada is lagging so far behind other countries is that business investment in this country is lagging. Canadian businesses are just not investing enough in equipment and technology. Technology like artificial intelligence or AI. This is something Jean-Sébastien Charest has been paying close attention to. Jean-Sébastien is the Chief Information Officer for BDC, the Business Development Bank of Canada, and we're going to hear from him in a minute. But first we're going to get a quick primer on productivity from CPA Canada's chief economist, David-Alexandre Brassard. I mentioned Canada lags behind other countries, and honestly that's maybe too generous. Canada is more of a distant figure in the rear view mirror of countries like the US.

David-Alexandre Brassard:

If we compare ourselves to the US Labor that's producing $1 of goods and services in Canada could produce $1.20 in the two thousands. Right now, it can produce $1.30 in the US, so that's a big difference. That's a 30% gap. The US has always been historically strong with productivity growth and some European countries are keeping up. We're not part of that lot, so we're not keeping up with those countries.

Neil Morrison:

And the thing is productivity matters a lot. In fact, if you're the CFO of a company, it's maybe the one thing that matters most.

David-Alexandre Brassard:

The idea of productivity is simply doing more with less input. So you produce more with less input. Most of the time when we talk about productivity, we often talk about labor productivity, so that means producing more goods and services with less people doing the actual work.

Neil Morrison:

But productivity also matters beyond the balance sheet, it's essential to the economy.

David-Alexandre Brassard:

Essentially, economic growth relies on productivity, because we cannot grow our population endlessly. So you need to do more with less people. That's kind of part of the bargain if you want our standards of living to improve.

Neil Morrison:

That was David-Alexandre Brassard, CPA, Canada's chief Economist. Now I mentioned we were going to hear from Jean-Sébastien Charest, the chief information officer of BDC. He points to another way Canada trails other countries. It's not just productivity. It's in fact one of the keys to improving productivity.

Jean-Sébastien Charest:

We know that AI can play a role in helping us fill that gap in terms of productivity. There was this pretty interesting study that came out of DAIS, which is a policy think tank out of Toronto Metropolitan University that says that audited 35 OECD countries that have national statistical agencies that have conducted these types of business surveys, well Canada ranks 20th in AI adoption per se. So we have a productivity issue. And then when you look at AI, we are in the 20th on 35.

Neil Morrison:

And the problem with AI adoption is actually most pronounced in smaller companies, and that's what we're going to talk about on this episode. AI adoption in small and medium-sized businesses. And there's good reason for this. Most of the economy is made up of these enterprises, and it's where many, many CPAs work. And if adopting AI is part of the answer to closing the productivity gap, to improving the ratio of inputs to outputs, then CPAs will need to be part of the solution. And these businesses need all the help they can get.

Jean-Sébastien Charest:

When you look at smaller companies, let's say 5 to 19 employees, only 5.5% of those plan to adopt AI. But the thing is, when you look at Canada, the way that our economy is built around businesses, 98% of Canada's businesses have fewer than a hundred employees, and these companies are responsible for nearly 40% of the country's GDP. So if we want to have AI play a role in helping us fill that productivity gap as a whole, then you see that adoption of AI within small businesses will be required, or at least can play a major role for the Canadian economy itself. So that's sort of the lay of the land at this point.

Neil Morrison:

So small and medium enterprises are not adopting AI at anything close to the rate of other countries. Do we know why? How do small and medium enterprises in Canada view AI?

Jean-Sébastien Charest:

So in January, we released a new economical study from my colleague, the chief economist of BDC, Pierre Cléroux, on Canadian entrepreneurs on opening the door to AI. And it's pretty interesting, because in this study, clearly SMEs so small businesses, they believe that AI will have a positive impact on all facets of the business landscape in the next three to five years. They believe it'll have an impact on their business, their sector, the Canadian economy, and they predict a positive impact on all of these different facets. But when you look at their ability to start looking at how AI will change their own business or how they can start doing some first steps into adopting AI within their business, that's harder because only 59% of them are familiar with the goods and services that use AI. They're not able to have this first step between the awareness and then how do I translate this in a diagnostic of work? Where can I start?

Neil Morrison:

So the position of small and medium-sized businesses is actually very interesting. They're aware of AI, they think it will have implications for their businesses. They think that the impact will be positive, but they don't actually know what AI tools and services are available to them and that they can use. Is that correct?

Jean-Sébastien Charest:

Yes, exactly. So there is more education that is needed right now, and this is where going from awareness to actually taking action, there's a bit of, I would say, a concept here that is a bit sometimes maybe intimidating for small business. Because one key barrier may be the lack of technology knowhow within their business to begin with. These companies sometimes are too small to even have an IT department and IT leadership let alone having an IT strategy. So the concept of AI can be intimidating as well. So for them, I know it's out there, I know it's going to change my industry and the Canadian economy, but what does it mean for me? So from going from the what to the, so what is sort of a barrier right now that we need to tackle? Because there are many ways for them to start using AI and not only in what we think about how AI is very revolutionizing, or I don't know if you say that in English, but it's a revolution. There's mundane uses of AI that can quickly help productivity.

Neil Morrison:

What are some examples of that?

Jean-Sébastien Charest:

Yeah, so we spoke with different clients and there's this client, Marty Fisher, who's the co-CEO of a company called Show and Tell. They are headquartered in Winnipeg and it's an advertising and marketing agency. So these guys have been sharing with us their first usage of AI. For them, it meant sometimes just if they feel stuck or they want to start drafting a text, they get out of the white page and they start using AI to just generate a first decent draft, and then they can edit and refine this draft. So it gets you started. So this is the type of thing that any business can start using. And for their particular industry, they're also using AI to have AI create an image. For instance, if they want to really start a first pitch deck, they'll provide a precise description to the AI that'll then generate an image.

Then the agency's graphic designers, they can pick up this image, they can modify it, they can present first mockups during the pitch, and then if it goes forward, then the approved image during the pitch can then be used to brief their professional photographer and then they'll have originals, copyrightable photographs taken from there. But this is where AI accelerated and maybe put them in a competitive advantage of getting faster out the gate of that pitch and then getting their graphic designers and their photographers and their professionals working on the right things without having to do the initial repetitive work and non-value added tasks that maybe if the pitch doesn't go true, well, you didn't waste time in starting to design a first graphic. So these are the types of usage that are particular to their industry. But in this study, there's a bunch of examples of how these folks have started using AI in ways that are as approachable for any small business.

Neil Morrison:

How about non-generative AI? Is that also something that businesses are seeking out or can help with businesses?

Jean-Sébastien Charest:

All types of AI can help businesses, and this is where I'm going to put a bit of a caveat, right?

Neil Morrison:

Yeah.

Jean-Sébastien Charest:

AI, it's not a solution looking for a problem. Of course, AI can help if it's aligned with the objectives and the goals of the company. So it's really about thinking of the business goals for the next year or two and where are the most pressing needs, and then how can digital technologies address them. Sometimes AI may be the ultimate answer, but maybe in your processes or maybe just a simple automation of your processes or cleaning your data. So sometimes yes, other types of AI of course will bring value in the long run.

Why are we talking a lot about generative AI in the past year? It's just because it democratizes the access to AI, because now you don't need to invest a lot of money, infrastructure, talent and then take major risks to get your business to start using it. Because the generative AI usage that we see right now is, I'll say, out of the box usage on a lot of technology that you already have or that are easy to integrate within your business. So if you go to more, it's called limb sophisticated users of AI, like machine learning, you need a lot of data. You need to feed this data into a very-

Neil Morrison:

You need specialists who understand how to do that. You're a small or medium enterprise. Maybe hiring a data scientist is not something you're able to do.

Jean-Sébastien Charest:

No, and then having the proper infrastructure, cloud infrastructure, think about data privacy and stuff like that. So I like to think about three horizons when you think about adopting AI. I think for about any businesses that can have a long-term value with AI. So your first horizon is sort of: can I use something that I have no differentiating factor? It's not going to reinvent something like drafting emails or writing minutes for a meeting or generating social media posts that'll bring stickiness, or helping me summarize documents. So these are all types of usage that you have no competitive advantage of tailoring yourself. You just pick them out of the box and you use them and they're a commodity at this point. So starting to integrate these capabilities within your organization will start working also on what I like to call your organizational muscles.

Neil Morrison:

Just develop the culture around it.

Jean-Sébastien Charest:

Develop the culture, and maybe start attracting employees that want to use this. So then once you get your basis right, you have your policies, now you start using it and you do first models, and then you start to see, well, can this be tailored to my needs? And also when you talk about generative AI, that is available out there. If you start training the models on your data, then the competitive advantage can come in. Because now the AI is going to give you more tips and advice and indicators that are based on your data, probably maybe based on the way you've been doing business. So this is where horizon two can start helping you into having a competitive advantage. So this is more on a six months, one year once you've started using AI. And then horizon three is like-

Neil Morrison:

Just one sec. I'm going to just recap. So horizon one is just begin integrating it, working with it, exploring it, building a culture where people are interested in, to some extent enjoying it and finding uses for it, develop some of the policies around its use. Step two, horizon two, is to begin looking at ways that it can obviously help you. Is that right? Those are the two horizons so far.

Jean-Sébastien Charest:

Yeah. But the second horizon will ask you to start building your own models. So that's where you'll need to either build your own data lake or at least work with a company if you don't have the internal staff to do it. But this is where you start shaping something that is made, tailored for your own business needs. But then again, it's part of the many other things you're doing in the organization. It's another type of decision-making tool that you will integrate. So that's step two. And then moving to sort of a horizon three is like, well now do you really feel that there is something out there for you to disrupt the market or be a market shaper? And then this would be a very finite number of companies that will start wanting to integrate, let's say AI within their own products that they deliver to their clients or having AI at the very core of their enterprises.

Then this is really something we're going to see in the future because everybody got to use the same toolkit at the same time in the past year. So we're going to start seeing more and more companies build their strategy around this, where it's not only going to be one more tool in your tool chest, but it's going to be your core business will refocus around this. This is on a sort of longer horizon, but for now, we need more and more. We need more and more players to start adopting it, and this is the area where about every business can start.

Neil Morrison:

So step three is the AI actually becomes part of the product, part of what you're offering to clients, to customers. You're actually offering an AI solution somehow embedded in the product.

Jean-Sébastien Charest:

Either offering it or basing your own manufacturing production, operations, everything in your business, even for your internal needs, right? Now, a lot of your decisions are taking with this AI that you've built for you internally. So these are going to be future usage that we're going to see, but it's going to take a bit of time before we get there. For others than major players out there that have a competitive advantage of. Because gen AI is pretty recent, but a lot of the major players out there have been playing around with AI for quite a bit right now. Yeah.

Neil Morrison:

Okay. Going back before you gave us the three horizons or the three steps for the evolution of how businesses incorporate AI into their business process, you mentioned that AI is not a... How did you phrase it? It's not a solution in search of a problem. Can you just tease that out a bit? How does that look for a business when they're trying to figure out, okay, let's start our AI journey?

Jean-Sébastien Charest:

So when I say AI is not a solution looking for a problem, is that you really need to make sure of a couple of things. You need to make sure it's aligned with your business strategy and you really understand what objective you are chasing. You need to have built in measurable ways to have quantitative results of your AI. If day one, you're not able to say, this is what I'm trying to reach in a quantitative way, and your AI is not built in a way that you can measure how much value you're adding as you go through, well, it's going to be easy to lose sight between thinking that you're doing the right thing and knowing that you're doing the right thing. So if I'm saying I want this to be able to increase 7% retention of clients in this geography, while your AI needs to be able to have day one built within the model, how am I doing on that 7% and how do I think that right now my whatever predictions comes out of the models will help me get closer to that 7% in a quantitative manner.

Neil Morrison:

So develop concrete quantitative goals, and then what's next?

Jean-Sébastien Charest:

And then you need to be able to have adoption, because in these things, adoption is key. You could build the best model to say, Hey, this is how I'm going to be able to retain my clients in this geography. But if the sales folks don't understand, have access to the tool or know how to use this tool, if you bring them a fancy dashboard and they don't use it on their day-to-day operations, you'll just have a fancy model sitting in some tool that is not used. So number two for me is you need adoption. So for adoption, you need training. You need to be able to work closely with these people, and that's where having quantitative objectives will help you because your salespeople, for instance, will know that using this tool will help them reach their own objectives. Then adoptions will start playing. And then number three I'd say is feedback loops, right?

So you've built this model, you have adoption, people have started to use it. Are you able to get feedback of being able to bring back this feedback loop in the way you're going to tailor or build your next model? So it really fits the needs of the people using the model on the tail end so that you have this value chain right now of you build the model, you have people using it, they give you your feedback, you integrate the feedback back to the model, and then you start really seeing real results with the adoption of these new capabilities. Yeah.

Neil Morrison:

Okay. As we close here, I have in my mind the small, medium-sized business that you described is the bulk of our economy. Small business owners don't have a lot of time. They're extremely busy. The idea of bringing in something new like this is potentially overwhelming. It's a sense of, oh, I just don't have time to go in a completely new direction. How do you talk to them about just getting through that overwhelm and taking the right first step?

Jean-Sébastien Charest:

So I'm talking with an entrepreneur, and he or she is loaded with different tasks and has a lot of administrative processes within their company, let's say for instance. So if you have processes and you have administrative work within your organization, AI could help you free up some time, could help you be more productive. And then it can be simple things such as drafting a policy, or an internal employee manual. You want to post a job, it can quickly write a job description for you, get you started. You want to pitch to your clients, and you're always using the same sort of core for your pitch deck, but then you adapt it to the different clients you're going to pitch. AI can help you quickly adapt your basic pitch deck to the data you're going to feed them for this particular client and stuff like that.

So if you are busy and you have a lot of work, well, first step is seeing how AI can help you on these very small tasks, but that can start freeing up some time in your agenda so that you focus on more value added tasks.

Neil Morrison:

So small steps.

Jean-Sébastien Charest:

Small steps. And once you start using these small steps, maybe it's going to help you picture how bigger steps within even your internal operations AI can help you. Yeah.

Neil Morrison:

Jean-Sébastien, I've really found this helpful. Thank you so much for taking the time to chat.

Jean-Sébastien Charest:

Of course, anytime. Thank you, Neil.

Neil Morrison:

Jean-Sébastien Charest is the Chief Information Officer for the Business Development Bank of Canada. On our next episode we will be speaking with Ryan Meyers. Ryan is the head of product at Manifest Climate which uses AI to help companies identify gaps in their sustainability performance. On our first episode this season, Zohaib Akhtar mentioned that AI could help companies with sustainability reporting so we thought we’d take a deeper dive into it. Manifest Climate uses a large language model in its AI. Chat GPT is an example of a large language model. Ryan says CPAs need to quickly become comfortable using tools like Chat GPT and AI because these tools are evolving quickly. He thinks it won’t be long before they become a basic tool in the CPA toolbox.

“GPT-IV is not the end, it's just the beginning right? And you know, there's Google's Gemini and there's a new version of Gemini coming out soon. GPT-IV and a half will be coming out. So, it's only gonna get better for sure and I think, if I were a CPA, the way I would be thinking about this is this is a great tool to be used. It's only gonna get better. It's not going anywhere. So I think it's really important to gain some sort of exposure to it. Like I would start with just using chat GPT. But if your company's not already working on it, they should be building an internal version of GPT that is secure for your data. And I think everybody should be learning how to use it.”

That’s Ryan Meyers, the head of product at Manifest Climate.

And that's it for this episode of Foresight, the CPA podcast. If you like what you heard, please give us a five star rating or review wherever you get your podcasts and share it through your networks. Foresight is produced for CPA Canada by Pod Craft Productions, and please note, the views expressed by our guests are theirs alone and do not necessarily reflect the views of CPA Canada. Thanks so much for listening. I'm Neil Morrison.